Artificial Intelligence Application in Algorithmic Trading: Two-day Workshop

16 & 18 October 2018

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With hundreds of prices flashing on and off the electronic platforms every millionth of second and unforeseeable market-moving news hitting the screens at any moment, to capture a perfect trade at lightning speed can no longer be accomplished by mere numeric computation or formulae-driven programmes, not to mention the obsolete reliance on human naked eyes and nimble fingers. With the evolution of AI technology picking up speed in the past decade, automation of trading has been empowered by advanced algorithms that teach machines to perform reasoning, data crunch, news analysis, trade decision making, price capturing, and trade execution the human way but all in less than a millionth second or even in nano-second.

This is a two-day rigorous and intensive program which will sow the seeds for traders wanting to pursue research in how data science can be applied to trading in the financial markets.
Who should attend: trader, portfolio manager, individual investor, college students from finance, investing, data science, quantitative finance, financial engineering background.
Students are required to bring their laptops.
Day-one will start with an introduction session (refer to Part One of Course Content) for both heads of trading/managers and traders/participants to know what the traders will benefit from the workshop
Several algorithms will be discussed with examples on how we can apply them to trading.

Course Details (Subject to changes)

Workshop Dates	:	16 & 18 October, 2018 (Tue & Thu), 9:00am - 5:00pm
Venue for Workshop	:	SGX Centre, SGX Academy Room 1&2, 2 Shenton Way, Singapore 068804
Fees	:	S$400 (SBMA members); S$500(non-SBMA members), Price is inclusive of 2 coffee breaks for both days of workshop
Cancellation policy	:	We reserve the right to cancel or re-shcedule the program. In case of cancellation, we will refund 100% of the fee duly received

Course Content

Part One: Introduction to Artificial Intelligence
What is A.I.? Why now? Machine Learning – major models Deep Learning – major models Assessing model performance Pitfalls: Shortcoming and expensive errors Key takeaway: Flash cards where algorithms are defined in a few lines with no (almost no) mathematical reference.	Trading Heads/Managers are also welcomed (complimentary) to attend this session. We will make a quick introduction to AI technology to those who do not have any prior exposure to AI algorithms, and who are capable of driving change in their organization and want a quick adequately detailed understanding of what is in the algorithms.
Part Two: Artificial Intelligence Rigor
Algo Suite One Logistic regression Linear discriminant analysis Regularization Key takeaway: Practical implementation of Algorithm to trade gold futures in any time frame. Algo Suite Two Decision trees Ensemble methods Support vector machines Key takeaway: Practical Implementation of Algorithm to trade correlation. Algo Suite One Neural networks Multilayer perception and hyper parameter tuning Convolutional networks Recurrent neural networks Unsupervised learning algorithms Key takeaway: Practical implementation of Algorithm to trade listed options.	This session is for traders. We will cover all the major algorithms in detail and deliver an intuition behind the mathematics of each algorithm. We will implement live programing exercises with participants. This is for participants who want to launch themselves into the rigorous application of machine learning in trading financial instruments.

Speakers

Avirath Kakkar Avirath is the head of strategy at EIS Global, an algorithmic trading firm trading liquid financial markets across the globe. Avirath is obsessed with the application of data science to financial markets. He spends huge amount of his time on portfolio optimization and in the search of alpha through the application of data science algorithms. He started this journey in 2014 and has spent countless hours understanding and applying decision trees, neural networks, memory models, reinforcement learning algorithm's and the like. He firmly believes the extent and scope of what data science can do is near infinite. Prior to starting his firm in 2016, Avirath was a fund manager at Balyasny Asset Management co-running a portfolio with USD 350mm under management. Before Balyasny, he worked at JP Morgan in the commodity structuring and at Standard Bank managing precious metal global option book out of Asia.

Arvin Sahni Arvin has 10 years of work experience in data science, financial markets and software engineering. She started her career with Oracle India as a member of the e-business suite application development team. After completing her MBA, she joined Citibank in Singapore specializing in providing structured investor solutions for commodity assets before moving to a sales role covering both corporate and institutional clients from the metals markets in APAC ex Japan and Australia. With a strong career graph thus far, Arvin decided to shift her focus to her passion in Data Sciences. She enrolled into the UC Berkeley's Master in Information and Data Science. While pursuing her masters, Arvin joined Limnah Capital Pte Ltd., a macro hedge fund, as a board member with the responsibility of developing algorithmic trading strategies based on machine learning.

Tianyi Zhang Tianyi recently joined EIS as a research associate. He was offered full scholarship to study in National University of Singapore majoring in electrical engineering. During his study, he found his interests in Data Science and pursued a Master in statistics at NUS therefore. With the passion in applying Machine Learning to financial market, he joined EIS after his graduation.

For more information, please visit sbma.org.sg or contact Lynn at +65 6823 8011 / lynn.yap@sbma.org.sg.

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